Computer assisted dispatch incident report video search and tagging systems and methods

ABSTRACT

A video tagging method, a video analytics method, and a Computer Assisted Dispatch (CAD) system use incident reports for video tagging and searching. A CAD system is integrated with field-based video surveillance systems to aid in searching video content and subsequent tagging of the video content. The systems and methods can include extracting keywords from a CAD incident report and creating a small, focused, and incident-specific dictionary on which to perform video searching in real-time. The small, focused, and incident-specific dictionary increases tagging accuracy and reduces tagging processing time in the context of video analytics. Further, a multi-pass approach in real-time continually updates and disseminates to video cameras of interest the small, focused, and incident-specific dictionary as the CAD incident report is updated and as the incident of interest plays out.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to video tagging and analyticsand more particularly to systems and methods for using Computer AssistedDispatch (CAD) incident reports for video tagging, searching, analytics,etc.

BACKGROUND

Video surveillance systems and methods are being deployed and networkedthroughout various geographical regions. For example, mobile digitalvideo recorder (MDVR) in the public safety context (e.g., police, fire,medical, etc.) can be located on a vehicle dashboard, on public safetypersonnel or the like, fixed on a pole for example, and the like. As canbe expected with the proliferation of video surveillance systems andmethods, significant amounts of video are being recorded and capturedmaking it increasing important to index recorded video to make itsearchable by features. Further, it would be helpful to pre-filter thevideo prior to upload and/or saving/storing. In a MDVR used in a publicsafety role, there is often an incident report that is in effect forperiods of time during which video is being captured. Computer AssistedDispatch (CAD) (also referred to as Computer Aided Dispatch) systems andmethods can be used to generate the incident reports. Typically adispatcher announces relevant call details to the field personnel whotake action accordingly. Conventionally, there is no real-timeintegration of CAD incident reports and field-captured video.

Video analytics generally refers to processing and searching video suchas to detect something contained therein on one or more frames ofcaptured video. Conventionally, video analytics is difficult due to allof the possible permutations of objects, people, etc. that could becontained in each video frame. Specifically, all items that can be invideo frames in the context of video analytics can be contained in aso-called dictionary. That said, processing is complex andtime-consuming with conventional dictionaries being extremely large.

Accordingly, with the vast proliferation of video captured in the field,there is a need for video tagging and searching systems and methods toassist in retrieval, storage, and/or analytics.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying figures, where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, together with the detailed description below, are incorporated inand form part of the specification, and serve to further illustrateembodiments of concepts that include the claimed invention, and explainvarious principles and advantages of those embodiments.

FIG. 1 is a network diagram of a video tagging system in accordance withsome embodiments.

FIG. 2 is a flowchart of a video tagging method based on an incidentreport and an associated incident-specific dictionary in accordance withsome embodiments.

FIG. 3 is a flowchart of a video tagging method for capturing videousing the incident-specific dictionary in accordance with someembodiments.

FIG. 4 is a flowchart of a video analytics method tagging video based onan incident report and an associated incident-specific dictionary inaccordance with some embodiments.

FIG. 5 is a flowchart of a video analytics method for storing andsearching video based on the incident report and the associatedincident-specific dictionary in accordance with some embodiments.

FIG. 6 is a block diagram illustrates an exemplary implementation of aserver for the CAD system in accordance with some embodiments.

FIG. 7 is a block diagram of an exemplary implementation of a camerawhich is one component in the video capture system in accordance withsome embodiments.

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendrawn to scale. For example, the dimensions of some of the elements inthe figures may be exaggerated relative to other elements to help toimprove understanding of embodiments of the present invention.

The apparatus and method components have been represented whereappropriate by conventional symbols in the drawings, showing only thosespecific details that are pertinent to understanding the embodiments ofthe present invention so as not to obscure the disclosure with detailsthat will be readily apparent to those of ordinary skill in the arthaving the benefit of the description herein.

DETAILED DESCRIPTION

In various exemplary embodiments, systems and methods are describedusing Computer Assisted Dispatch (CAD) incident reports or the like forvideo tagging, searching, analytics, etc. In an exemplary embodiment,the systems and methods integrate CAD with video surveillance systems toaid in searching video content and subsequent tagging of the videocontent for video analytics. For example, the systems and methods caninclude extracting keywords from a CAD incident report and creating asmall, focused, and incident-specific dictionary on which to performvideo searching in real-time or later. Advantageously, the small,focused, and incident-specific dictionary increases tagging accuracy andreduces tagging processing time with respect to video analytics.Further, the systems and methods can include a multi-pass approach inreal-time that continually updates the small, focused, andincident-specific dictionary as the CAD incident report is updated andas the incident of interest plays out. This can be used for real-time,near real-time, or later processing of captured video. The CAD systemcam be communicatively coupled to a video surveillance system fordissemination of the small, focused, and incident-specific dictionarybased on individual camera's relevance to the incident of interest.

As described herein, tagging generally refers to associating keywordssuch as in an incident-specific dictionary or the like with videometadata. With this tagging, searching can include algorithmic searchingof captured video utilizing the incident-specific dictionary such asfinding anything in the captured video related to the incident-specificdictionary or searching the captured video based on search terms. In anexemplary aspect, the systems and methods greatly improve videoanalytics by significantly reducing the associated dictionary, i.e. theincident-specific dictionary, based on terms gathered from an incidentreport, i.e. a CAD incident report or the like. With a reduceddictionary that is relevant to an incident, video analytics can be apowerful tool.

In an exemplary embodiment, a video tagging method based on an incidentreport and an associated incident-specific dictionary includes capturingkeywords from the incident report related to an incident of interest ata first time period; creating an incident-specific dictionary for theincident of interest based on the captured keywords; providing theincident-specific dictionary from the first time period to at least onevideo camera capturing video based on a plurality of factors or aback-end server communicatively coupled to the at least one videocamera; and utilizing the keywords from the incident report by the atleast one video camera to tag captured video.

In another exemplary embodiment, a video analytics method includesdeveloping an incident-specific dictionary based on an incident reportrelated to an incident of interest, wherein the incident-specificdictionary includes a focused list of objects and people based on theincident report; receiving the incident-specific dictionary at a firstcamera based on geographic proximity to the incident of interest;capturing video by the first camera; tagging the captured video by thefirst camera with keywords in the incident-specific dictionary; andanalyzing the captured video using the keywords in the incident-specificdictionary to perform video analytics based on the focused list.

In yet another exemplary embodiment, a Computer Assisted Dispatch (CAD)system includes a network interface communicatively coupled to anetwork; a processor communicatively coupled to the network interface;memory including instructions that, when executed, cause the processorto: capture keywords from an incident report related to an incident ofinterest at a first time period; create an incident-specific dictionaryfor the incident of interest based on the captured keywords; provide,via the network interface, the incident-specific dictionary from thefirst time period to at least one video camera capturing video based ona plurality of factors; capture updated keywords as the incident ofinterest unfolds at a second time period; update the incident-specificdictionary with the updated keywords; and provide the updatedincident-specific dictionary to another video camera or a back-endserver communicatively coupled to the another video camera a based onthe plurality of factors.

Referring to FIG. 1, in an exemplary embodiment, a network diagramillustrates a video tagging system 10. Specifically, the video taggingsystem 10 includes a CAD system 12, a video capture system 14, a network16, and a wireless network 18. The CAD system 12 is communicativelycoupled to the video capture system 14 via the network 16, the wirelessnetwork 18, and/or other components or networks not illustrated herein.The CAD system 12 is configured to generate CAD incident reports. Asdescribed herein, the CAD incident reports can include messages withinformation such as locations, people of interest, generalized subjectdescription, event synopsis, responding units, or any other relevantinformation to the incident. The CAD incident reports can also beupdated as associated incidents unfold. Specifically, the CAD system 12can include dispatchers, i.e. CAD operators or dispatchers,communicatively coupled to servers 20 which ultimately connect to thevideo capture system 14 via the networks 16, 18.

The video capture system 14 includes a plurality of cameras andassociated processing components for capturing real-time video over ageographic region. The cameras can include MVDRs. Also, the cameras canbe located on first responders or in first responders' vehicles. Evenfurther, the cameras can be surveillance cameras disposed about thegeographic region such as on street lights, on sign posts, on and/or inbuildings, etc. Each camera is configured to capture video andoptionally audio and the processing components are configured to processthe video as is described herein. Specifically, the processingcomponents can include tagging video with keywords from anincident-specific dictionary from the CAD system 12 thereby associatingthe keywords with captured video metadata. Further, the processingcomponents can be configured to search real-time or close to real-timecaptured video and/or audio looking for items in the incident-specificdictionary as well as notifying personnel once detection occurs.

The CAD system 12 can include a suite of software packages used toinitiate public safety calls for service, dispatch, and maintain thestatus of responding resources in the field. The CAD system 12 can begenerally used by emergency communications dispatchers, call-takers, and911 operators in centralized, public-safety call centers, as well as byfield personnel utilizing mobile data terminals (MDTs) or mobile datacomputers (MDCs). The CAD system 12 can include several functionalsoftware modules executed on servers 20 that provide services atmultiple levels in a dispatch center and in the field of public safety.These functional modules can include call input, call dispatching, callstatus maintenance, event notes, field unit status and tracking, andcall resolution and disposition. The CAD system 12 can also includeinterfaces that permit the software to provide services to dispatchers,call takers, and field personnel with respect to control and use ofanalog radio and telephony equipment, as well as logger-recorderfunctions.

The CAD incident reports are sent from the servers 20 to dispatchees inthe field. For example, the dispatchees can include public safetypersonnel or the like. The dispatchees can have a mobile device that isused to display and/or play the CAD incident reports. The mobile devicecan also be used for a plurality of additional purposes, e.g. store andretrieve data, communicate on the networks 16, 18, etc. The mobiledevices can include MDTs, MDCs, smart phones, tablets, two-way radios,etc. The CAD incident reports can also be updated by both the dispatcherand/or the dispatchees as the associated incident unfolds.

For example, an exemplary CAD incident report can be as follows:LOCATION: 1234 Main St. REPORTING John Doe, 555-5555, 4567 Main St.PARTY: TIMESTAMP: 4:01:03 EDT, 8/24 INCIDENT: Armed Robbery (inprogress) SYNOPSIS: “Caller reports Quick Mart convenience store beingheld up/Caller advises suspect is in a white 2 door sedan speeding awayheading west.” The aforementioned CAD incident report can be sent viathe servers 20 to a plurality of dispatchees, such as police officers.Subsequently, police officers may be tagged to or included in the CADincident report as follows: LOCATION: 1234 Main St. REPORTING John Doe,555-5555, 4567 Main St. PARTY: TIMESTAMP: 4:02:33 EDT, 8/24 INCIDENT:Armed Robbery (in progress) SYNOPSIS: “Caller reports Quick Martconvenience store being held up/Caller advises suspect is in a white 2door sedan speeding away heading west.” UNITS: 746 (On scene), 749 (inroute) In the above, the CAD incident report has units, i.e. units 746,749, tagged as responding units. These units could add additionaldetails as the incident unfolds, such as: LOCATION: 1234 Main St.REPORTING John Doe, 555-5555, 4567 Main St. PARTY: TIMESTAMP: 4:04:23EDT, 8/24 INCIDENT: Armed Robbery (in progress) SYNOPSIS: “Callerreports Quick Mart convenience store being held up/Caller advisessuspect is in a white 2 door sedan speeding away heading west/Unit 746in pursuit of the white 2 door sedan heading north on South Blvd/Suspectdriver is wearing a red jacket and glasses.” UNITS: 746 (in pursuit),749 (in route)

As can be seen in the aforementioned unfolding CAD incident report,there is geographical data (e.g., 1234 Main St., speeding away headingwest, heading north on South Blvd), timestamp information, andmeaningful keywords (e.g., white 2 door sedan, red jacket, glasses,etc.).

In various exemplary embodiments, the CAD system 12 is configured toextract keywords from the CAD incident report, creating a small,focused, incident-specific dictionary on which to perform video searchand to tag capture video. For example, in the aforementioned unfoldingCAD incident report, the incident-specific dictionary may include white2 door sedan, red jacket, glasses as well as various geographicdescriptions, i.e. 1234 Main St., speeding away heading west, headingnorth on South Blvd. Note it is expected that the incident-specificdictionary will differ for each incident. A reduced incident-specificdictionary increases tagging accuracy and reduces tagging processingtime. Further, the reduced incident-specific dictionary streamlines andoptimizes video analytics. Also, the incident-specific dictionary iscontinually evolving as the incident of interest plays out, i.e. amulti-pass approach as CAD incident report is updated.

Further, the incident specific dictionary is disseminated to variouscomponents in the video capture system 14 based on the componentsrelevance to the incident. For example, the dictionary could be providedto a camera associated with the responding officers or officers inproximity and any cameras that are in the geographic vicinity, e.g.street light cameras, surveillance cameras, etc. Additionally, thedictionary could be provided to cameras and the like in the videocapture system 14 based on other metrics such as cameras farther awayfrom the incident of interest but along probable routes for fleeingsuspect, all cameras in a certain geographic region, etc. For example,the dictionary could be provided to various cameras in a geographicregion with an ever expanding radius based on time, probable speed of asuspect or vehicle, etc. The video tagging system 10 can use algorithmsto ensure the dictionary is provided to any relevant video capturesystem 14 to the incident.

Taking the aforementioned unfolding CAD incident report as an example,at a first point, timestamp 4:01:03 EDT, 8/24, the dictionary wouldinclude Quick Mart convenience, white 2 door sedan, etc., and thisdictionary would be provided to cameras in the vicinity of the location,1234 Main St. At a second point, timestamp 4:02:33 EDT, 8/24, thedictionary stays the same, but additional cameras may receive theupdated dictionary, i.e. cameras associated with units 746 and 749. At athird point, timestamp 4:04:23 EDT, 8/24, the dictionary can be updated,at that point forward, to include Suspect driver is wearing a red jacketand glasses, etc. and the dictionary can be further provided toadditional cameras, i.e. cameras in a northward direction on South Blvd.Thus, the video tagging system 10 includes an evolving, dynamicallyupdated incident-specific dictionary managed by the CAD system 12 thatis provided over time to geographically relevant cameras in the videocapture system 14. Further, the dynamically updated incident-specificdictionary can also be applied to older video to improve analyticsthereon. That is, previously captured video from geographically relevantcameras can be provided the updated incident-specific dictionary fortagging thereon.

The video capture system 14 can use the incident-specific dictionary forefficient video segment search, identification, video analytics,prioritization and targeted video segment sharing. Specifically, camerasand associated processing devices in the video capture system 14 can usethe incident-specific dictionary for tagging captured video whichincreases tagging accuracy and reduces tagging processing time in thecontext of video analytics. Overall, this provides efficiency, speed,and optimization in the video capture system 14, prioritizing videosegments and allowing video to be culled, reducing storage requirementsin mobile units for the video capture system 14. It is expected thisintelligence in the video capture system 14 will make it more operatorfriendly, tightly coupling video content to incident reports and makingvideo more searchable after uploading with the tags from the dictionary.Thus, the system 10 combines video tagging with a restricted dictionaryof keywords derived from real-time dynamically updated CAD incidentreport, allowing for efficient video segment search, identification,video analytics, prioritization and targeted video segment sharing

Referring to FIG. 2, in an exemplary embodiment, a flowchart illustratesa video tagging method 30 based on an incident report and an associatedincident-specific dictionary. In an exemplary embodiment, the videotagging method 30 can be implemented by the video tagging system 10. Thevideo tagging method 30 includes capturing keywords from the incidentreport related to an incident of interest at a first time period (step31). The video tagging method 30 further includes creating anincident-specific dictionary for the incident of interest based on thecaptured keywords (step 32). The video tagging method 30 furtherincludes providing the incident-specific dictionary from the first timeperiod to at least one video camera capturing video based on a pluralityof factors or a back-end server communicatively coupled to the at leastone video camera (step 33). The plurality of factors determines whichcameras receive the incident-specific dictionary. For example, thekeywords can be categorized as location-related keywords andincident-related keywords. The plurality of factors can include directresponders of the incident of interest, nearby responders of theincident of interest, location based on the location-related keywords,and probable routes based on the location-related keywords. The videotagging method 30 can provide the incident-specific dictionary to thecamera and/or the back-end server associated with the camera.

The video tagging method 30 further includes utilizing the keywords fromthe incident report by the at least one video camera to tag capturedvideo (step 34). The video tagging method 30 can further includecapturing updated keywords as the incident of interest unfolds at asecond time period (step 35). The video tagging method 30 can furtherinclude updating the incident-specific dictionary with the updatedkeywords (step 36). The video tagging method 30 can further includeproviding the incident-specific dictionary subsequent to the updating toanother video camera based on the plurality of factors or a back-endserver communicatively coupled to the another video camera (step 37). Inan exemplary embodiment, the keywords are captured by the CAD system 12,the incident-specific dictionary is created for the incident of interestby the CAD system 12, and the CAD system 12 is configured to disseminatethe incident-specific dictionary and associated updates to the at leastone video camera in the video capture system 14 based on the pluralityof factors.

Referring to FIG. 3, in an exemplary embodiment, a flowchart illustratesa video tagging method 40 for capturing video using theincident-specific dictionary. The video tagging method 40 can be usedwith the video tagging method 30 and/or the video tagging system 10. Thevideo tagging method 40 includes receiving the incident-specificdictionary at the at least one video camera or the back-end servercommunicatively coupled to the at least one video camera (step 41). Thevideo tagging method 40 further includes capturing video by the at leastone video camera (step 42). The video tagging method 40 further includestagging the captured video with the keywords from the incident-specificdictionary (step 43). The video tagging method 40 further includesuploading the captured video with the tagged keywords (step 44). Thevideo tagging method 40 can further include searching a plurality ofvideos using the tagged keywords as search terms (step 45). The videotagging method 40 can further include analyzing the captured video fordetection based on the keywords (step 46). The video tagging method 40can further include providing an alert upon detecting one of thekeywords in a portion of the captured video (step 47). In an exemplaryembodiment, the at least one video camera includes a mobile digitalvideo recorder (MDVR). In another exemplary embodiment, the at least onevideo camera includes a surveillance camera and one of the plurality offactors includes proximity to the incident of interest.

Referring to FIG. 4, in an exemplary embodiment, a flowchart illustratesa video analytics method 50 tagging video based on an incident reportand an associated incident-specific dictionary. The video analyticsmethod 50 can be used with the video tagging system 10 and/or the videotagging methods 30, 40. The video analytics method 50 includesdeveloping an incident-specific dictionary based on an incident reportrelated to an incident of interest (step 51). The incident-specificdictionary includes a focused list of objects and people based on theincident report. That is, an objective of the video analytics method 50is to provide a reduced-sized dictionary for real-time or latersearching of video. Because the incident-specific dictionary is areduced-size dictionary, video analytics are improved and moreefficient. That is, the video analytics know what to look for based onthe incident report, e.g. a CAD report. This streamlines the videoanalytics significantly versus a conventional dictionary that caninclude several orders of magnitude more terms.

The video analytics method 50 includes receiving the incident-specificdictionary at a first camera or the back-end server communicativelycoupled to the first camera based on geographic proximity to theincident of interest (step 52). That is, the video analytics method 50can push the incident-specific dictionary to relevant cameras such thatthe incident-specific dictionary can be tagged with metadata of thevideo for real-time or later processing. The video analytics method 50includes capturing video by the first camera (step 53). The videoanalytics method 50 further includes tagging the captured video by thefirst camera or the back-end server with keywords in theincident-specific dictionary (step 54). As described above, here, thecaptured video can include associated metadata with theincident-specific dictionary.

The video analytics method 50 includes analyzing the captured videousing the keywords in the incident-specific dictionary to perform videoanalytics based on the focused list (step 55). Here, the video analyticsmethod 50 performs video analytics with the reduced-size dictionary.This analyzing step can be performed by the first camera in real-time ornear real-time as well as a server. The analyzing can include searchingthe captured video for terms in the incident-specific dictionary. Thissearching can be either algorithmic (e.g., find anything related to theincident-specific dictionary in the captured video) or based on searchterms (e.g., find a specific term). For example, assume theincident-specific dictionary includes a term for a “red car.” Thissignificantly improves the video analytic functionality as the analyzingknows to look at the captured video for a red car as opposed to lookingfor all cars, trucks, people, etc.

The video analytics method 50 can include receiving an updatedincident-specific dictionary at a second camera or a back-end servercommunicatively coupled to the second camera based on the updatedincident-specific dictionary (step 56). Here, the video analytics method50 can expand to other cameras as the incident unfolds. The videoanalytics method 50 can include capturing video by the second camera(step 57). The video analytics method 50 can include tagging thecaptured video by the second camera with keywords in the updatedincident-specific dictionary (step 58). Similar video analytics can beperformed for the captured video by the second camera as describedherein with respect to the first camera.

Referring to FIG. 5, in an exemplary embodiment, a flowchart illustratesa video capture method 60 for storing and searching video based on theincident report and the associated incident-specific dictionary. Thevideo capture method 60 can be used with the video tagging system 10,the video capture method 50, and/or the video tagging methods 30, 40.The video capture method 60 includes uploading the captured video by thefirst camera (step 61). The video capture method 60 further includesuploading the captured video by the second camera (step 62). The videocapture method 60 further includes storing the captured video by thefirst camera tagged with the keywords in the incident-specificdictionary (step 63). The video capture method 60 further includesstoring the captured video by the second camera tagged with the keywordsin the updated incident-specific dictionary (step 64). The video capturemethod 60 can further include searching video including the capturedvideo by the first camera and the captured video by the second cameraincluding search terms comprising the keywords in the incident-specificdictionary and the keywords in the updated incident-specific dictionary(step 65). In an exemplary embodiment, the first camera includes asurveillance camera in proximity to the incident of interest based onthe incident-specific dictionary and the second camera includes a mobilevideo data recorder associated with a responder based on the updatedincident-specific dictionary.

Referring to FIG. 6, in an exemplary embodiment, a block diagramillustrates an exemplary implementation of a server 100 for the CADsystem 12. The server 100 can be a digital computer that, in terms ofhardware architecture, generally includes a processor 102, input/output(I/O) interfaces 104, a network interface 106, a data store 108, andmemory 110. It should be appreciated by those of ordinary skill in theart that FIG. 6 depicts the server 100 in an oversimplified manner, anda practical embodiment may include additional components and suitablyconfigured processing logic to support known or conventional operatingfeatures that are not described in detail herein. The components (102,104, 106, 108, and 110) are communicatively coupled via a localinterface 112. The local interface 112 can be, for example but notlimited to, one or more buses or other wired or wireless connections, asis known in the art. The local interface 112 can have additionalelements, which are omitted for simplicity, such as controllers, buffers(caches), drivers, repeaters, and receivers, among many others, toenable communications. Further, the local interface 112 can includeaddress, control, and/or data connections to enable appropriatecommunications among the aforementioned components.

The processor 102 is a hardware device for executing softwareinstructions. The processor 102 can be any custom made or commerciallyavailable processor, a central processing unit (CPU), an auxiliaryprocessor among several processors associated with the server 100, asemiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. Whenthe server 100 is in operation, the processor 102 is configured toexecute software stored within the memory 110, to communicate data toand from the memory 110, and to generally control operations of theserver 100 pursuant to the software instructions. The I/O interfaces 104can be used to receive user input from and/or for providing systemoutput to one or more devices or components. User input can be providedvia, for example, a keyboard, touch pad, and/or a mouse. System outputcan be provided via a display device and a printer (not shown). I/Ointerfaces 104 can include, for example, a serial port, a parallel port,a small computer system interface (SCSI), a serial ATA (SATA), a fibrechannel, Infiniband, iSCSI, a PCI Express interface (PCI-x), an infrared(IR) interface, a radio frequency (RF) interface, and/or a universalserial bus (USB) interface.

The network interface 106 can be used to enable the server 100 tocommunicate on a network, such as to communicate with the video capturesystem 14 over the networks 16, 18. The network interface 106 caninclude, for example, an Ethernet card or adapter (e.g., 10BaseT, FastEthernet, Gigabit Ethernet, 10 GbE) or a wireless local area network(WLAN) card or adapter (e.g., 802.11a/b/g/n). The network interface 106can include address, control, and/or data connections to enableappropriate communications on the network. A data store 108 can be usedto store data such as captured video tagged with keywords from theincident-specific dictionary. The data store 108 can include any ofvolatile memory elements (e.g., random access memory (RAM, such as DRAM,SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM,hard drive, tape, CDROM, and the like), and combinations thereof.Moreover, the data store 108 can incorporate electronic, magnetic,optical, and/or other types of storage media. In one example, the datastore 108 can be located internal to the server 100 such as, forexample, an internal hard drive connected to the local interface 112 inthe server 100. Additionally in another embodiment, the data store 108can be located external to the server 100 such as, for example, anexternal hard drive connected to the I/O interfaces 104 (e.g., SCSI orUSB connection). In a further embodiment, the data store 108 can beconnected to the server 100 through a network, such as, for example, anetwork attached file server.

The memory 110 can include any of volatile memory elements (e.g., randomaccess memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatilememory elements (e.g., ROM, hard drive, tape, CDROM, etc.), andcombinations thereof. Moreover, the memory 110 can incorporateelectronic, magnetic, optical, and/or other types of storage media. Notethat the memory 110 can have a distributed architecture, where variouscomponents are situated remotely from one another, but can be accessedby the processor 102. The software in memory 110 can include one or moresoftware programs, each of which includes an ordered listing ofexecutable instructions for implementing logical functions. The softwarein the memory 110 includes a suitable operating system (O/S) 114 and oneor more programs 116. The operating system 114 essentially controls theexecution of other computer programs, such as the one or more programs116, and provides scheduling, input-output control, file and datamanagement, memory management, and communication control and relatedservices. The one or more programs 116 may be configured to implementthe various processes, algorithms, methods, techniques, etc. describedherein.

In an exemplary embodiment, the server 100 can be a CAD system, and thememory 110 can include instructions that, when executed, cause theprocessor to capture keywords from an incident report related to anincident of interest at a first time period, create an incident-specificdictionary for the incident of interest based on the captured keywords,provide, via the network interface 106, the incident-specific dictionaryfrom the first time period to at least one video camera capturing videoor a back-end server communicatively coupled to the at least one videocamera based on a plurality of factors, capture updated keywords as theincident of interest unfolds at a second time period, update theincident-specific dictionary with the updated keywords, and provide theupdated incident-specific dictionary to another video camera or aback-end server communicatively coupled to the another video camerabased on the plurality of factors. The instructions that, when executed,further can cause the processor to receive an alert from one of aplurality of video cameras that a keyword from the incident-specificdictionary or the updated incident-specific dictionary has been detectedin captured video. The instructions that, when executed, further cancause the processor to receive captured video from the at least onevideo camera with the captured video tagged with keywords from theincident-specific dictionary, and perform video analytics on thecaptured video using the incident-specific dictionary associatedtherewith.

Referring to FIG. 7, in an exemplary embodiment, a block diagram of anexemplary implementation of a camera 200 which is one component in thevideo capture system 14. The camera 200 can be a digital device that, interms of hardware architecture, generally includes a processor 202, avideo camera 204, a network interface 206, a data store 208, and memory210. It should be appreciated by those of ordinary skill in the art thatFIG. 7 depicts the camera 200 in an oversimplified manner, and apractical embodiment can include additional components and suitablyconfigured processing logic to support known or conventional operatingfeatures that are not described in detail herein. The components (202,204, 206, 208, and 210) are communicatively coupled via a localinterface 212. The local interface 212 can be, for example but notlimited to, one or more buses or other wired or wireless connections, asis known in the art. The local interface 212 can have additionalelements, which are omitted for simplicity, such as controllers, buffers(caches), drivers, repeaters, and receivers, among many others, toenable communications. Further, the local interface 212 may includeaddress, control, and/or data connections to enable appropriatecommunications among the aforementioned components.

The processor 202 is a hardware device for executing softwareinstructions. The processor 202 can be any custom made or commerciallyavailable processor, a central processing unit (CPU), an auxiliaryprocessor among several processors associated with the camera 200, asemiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. Whenthe camera 200 is in operation, the processor 202 is configured toexecute software stored within the memory 210, to communicate data toand from the memory 210, and to generally control operations of thecamera 200 pursuant to the software instructions. In an exemplaryembodiment, the processor 202 may include a mobile optimized processorsuch as optimized for power consumption and mobile applications. Thevideo camera 204 is configured to capture video and/or audio. This caninclude multiple cameras. Further, the video camera 204 can be fixed ormoveable (e.g., pan, tilt, zoom, etc.). The video camera 204 can capturevideo in a digital format according to various known techniques andstored the captured video in the data store 208 and/or upload thecapture video via the network interface 206.

The network interface 206 can be used to enable the camera 200 tocommunicate on a network, such as to communicate with the CAD system 12over the networks 16, 18. The network interface 206 can include, forexample, an Ethernet card or adapter (e.g., 10BaseT, Fast Ethernet,Gigabit Ethernet, 10 GbE) or a wireless local area network (WLAN) cardor adapter (e.g., 802.11a/b/g/n). The network interface 206 can includeaddress, control, and/or data connections to enable appropriatecommunications on the network. A data store 208 can be used to storedata such as captured video tagged with keywords from theincident-specific dictionary. The data store 208 can include any ofvolatile memory elements (e.g., random access memory (RAM, such as DRAM,SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM,hard drive, tape, CDROM, and the like), and combinations thereof.Moreover, the data store 208 can incorporate electronic, magnetic,optical, and/or other types of storage media.

The memory 210 can include any of volatile memory elements (e.g., randomaccess memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatilememory elements (e.g., ROM, hard drive, etc.), and combinations thereof.Moreover, the memory 210 may incorporate electronic, magnetic, optical,and/or other types of storage media. Note that the memory 210 can have adistributed architecture, where various components are situated remotelyfrom one another, but can be accessed by the processor 202. The softwarein memory 210 can include one or more software programs, each of whichincludes an ordered listing of executable instructions for implementinglogical functions. In the example of FIG. 7, the software in the memory210 includes a suitable operating system (O/S) 214 and programs 214. Theoperating system 214 essentially controls the execution of othercomputer programs, and provides scheduling, input-output control, fileand data management, memory management, and communication control andrelated services. The programs 214 can include various applications,add-ons, etc. configured to provide end user functionality with thecamera 200.

In the foregoing specification, specific embodiments have beendescribed. However, one of ordinary skill in the art appreciates thatvarious modifications and changes can be made without departing from thescope of the invention as set forth in the claims below. Accordingly,the specification and figures are to be regarded in an illustrativerather than a restrictive sense, and all such modifications are intendedto be included within the scope of present teachings.

The benefits, advantages, solutions to problems, and any element(s) thatmay cause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeatures or elements of any or all the claims. The invention is definedsolely by the appended claims including any amendments made during thependency of this application and all equivalents of those claims asissued.

Moreover in this document, relational terms such as first and second,top and bottom, and the like may be used solely to distinguish oneentity or action from another entity or action without necessarilyrequiring or implying any actual such relationship or order between suchentities or actions. The terms “comprises,” “comprising,” “has”,“having,” “includes”, “including,” “contains”, “containing” or any othervariation thereof, are intended to cover a non-exclusive inclusion, suchthat a process, method, article, or apparatus that comprises, has,includes, contains a list of elements does not include only thoseelements but may include other elements not expressly listed or inherentto such process, method, article, or apparatus. An element proceeded by“comprises . . . a”, “has . . . a”, “includes . . . a”, “contains . . .a” does not, without more constraints, preclude the existence ofadditional identical elements in the process, method, article, orapparatus that comprises, has, includes, contains the element. The terms“a” and “an” are defined as one or more unless explicitly statedotherwise herein. The terms “substantially”, “essentially”,“approximately”, “about” or any other version thereof, are defined asbeing close to as understood by one of ordinary skill in the art, and inone non-limiting embodiment the term is defined to be within 10%, inanother embodiment within 5%, in another embodiment within 1% and inanother embodiment within 0.5%. The term “coupled” as used herein isdefined as connected, although not necessarily directly and notnecessarily mechanically. A device or structure that is “configured” ina certain way is configured in at least that way, but may also beconfigured in ways that are not listed.

It will be appreciated that some embodiments may be comprised of one ormore generic or specialized processors (or “processing devices”) such asmicroprocessors, digital signal processors, customized processors andfield programmable gate arrays (FPGAs) and unique stored programinstructions (including both software and firmware) that control the oneor more processors to implement, in conjunction with certainnon-processor circuits, some, most, or all of the functions of themethod and/or apparatus described herein. Alternatively, some or allfunctions could be implemented by a state machine that has no storedprogram instructions, or in one or more application specific integratedcircuits (ASICs), in which each function or some combinations of certainof the functions are implemented as custom logic. Of course, acombination of the two approaches could be used.

Moreover, an embodiment can be implemented as a computer-readablestorage medium having computer readable code stored thereon forprogramming a computer (e.g., comprising a processor) to perform amethod as described and claimed herein. Examples of suchcomputer-readable storage mediums include, but are not limited to, ahard disk, a CD-ROM, an optical storage device, a magnetic storagedevice, a ROM (Read Only Memory), a PROM (Programmable Read OnlyMemory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM(Electrically Erasable Programmable Read Only Memory) and a Flashmemory. Further, it is expected that one of ordinary skill,notwithstanding possibly significant effort and many design choicesmotivated by, for example, available time, current technology, andeconomic considerations, when guided by the concepts and principlesdisclosed herein will be readily capable of generating such softwareinstructions and programs and ICs with minimal experimentation.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in various embodiments for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus the following claims arehereby incorporated into the Detailed Description, with each claimstanding on its own as a separately claimed subject matter.

What is claimed is:
 1. A video tagging method based on an incidentreport and an associated incident-specific dictionary, comprising:capturing keywords from the incident report related to an incident ofinterest at a first time period; creating an incident-specificdictionary for the incident of interest based on the captured keywords;capturing updated keywords at a second time period as the incident ofinterest unfolds; updating the incident-specific dictionary with theupdated keywords; providing the updated incident-specific dictionary toat least one video camera capturing video based on a plurality offactors or providing the updated incident-specific dictionary to aback-end server communicatively coupled to the at least one videocamera; and utilizing the keywords and updated keywords from theincident report by the at least one video camera to tag captured video.2. The video tagging method of claim 1, further comprising: providingthe incident-specific dictionary subsequent to the updating to anothervideo camera based on the plurality of factors or a back-end servercommunicatively coupled to the another video camera.
 3. The videotagging method of claim 1, wherein the keywords are categorized aslocation-related keywords and incident-related keywords.
 4. The videotagging method of claim 3, wherein the plurality of factors comprise anyof direct responders of the incident of interest, nearby responders ofthe incident of interest, location based on the location-relatedkeywords, and probable routes based on the location-related keywords. 5.The video tagging method of claim 1, wherein the keywords are capturedby a Computer Assisted Dispatch (CAD) system, wherein theincident-specific dictionary is created for the incident of interest bythe CAD system, and wherein the CAD system is configured to disseminatethe incident-specific dictionary and associated updates to the at leastone video camera based on the plurality of factors.
 6. The video taggingmethod of claim 5, further comprising: receiving the incident-specificdictionary at the at least one video camera or the back-end servercommunicatively coupled to the at least one video camera; capturingvideo by the at least one video camera; tagging the captured video withthe keywords from the incident-specific dictionary; and uploading thecaptured video with the tagged keywords.
 7. The video tagging method ofclaim 6, further comprising: searching a plurality of videos using thetagged keywords as search terms.
 8. The video tagging method of claim 6,further comprising: analyzing the captured video for detection based onthe keywords; and providing an alert upon detecting one of the keywordsin a portion of the captured video.
 9. The video tagging method of claim1, wherein the at least one video camera comprises a mobile digitalvideo recorder.
 10. The video tagging method of claim 1, wherein the atleast one video camera comprises a surveillance camera and one of theplurality of factors comprises proximity to the incident of interest.11. A video analytics method, comprising: developing anincident-specific dictionary based on an incident report related to anincident of interest, wherein the incident-specific dictionary comprisesa focused list of objects and people based on the incident report;receiving the incident-specific dictionary at a first camera or theback-end server communicatively coupled to the first camera based ongeographic proximity to the incident of interest; capturing video by thefirst camera; tagging the captured video by the first camera or theback-end server with keywords in the incident-specific dictionary; andanalyzing the captured video using the keywords in the incident-specificdictionary to perform video analytics based on the focused list;receiving an updated incident-specific dictionary at a second camera ora back-end server communicatively coupled to the second camera based onthe updated incident-specific dictionary; capturing video by the secondcamera; and tagging the captured video by the second camera withkeywords in the updated incident-specific dictionary.
 12. The videoanalytics method of claim 11, further comprising: uploading the capturedvideo by the first camera; uploading the captured video by the secondcamera; storing the captured video by the first camera tagged with thekeywords in the incident-specific dictionary; and storing the capturedvideo by the second camera tagged with the keywords in the updatedincident-specific dictionary.
 13. The video analytics method of claim12, further comprising: searching video comprising the captured video bythe first camera and the captured video by the second camera comprisingsearch terms comprising the keywords in the incident-specific dictionaryand the keywords in the updated incident-specific dictionary.
 14. AComputer Assisted Dispatch (CAD) system, comprising: a network interfacecommunicatively coupled to a network; a processor communicativelycoupled to the network interface; memory comprising instructions that,when executed, cause the processor to: capture keywords from an incidentreport related to an incident of interest at a first time period; createan incident-specific dictionary for the incident of interest based onthe captured keywords; provide, via the network interface, theincident-specific dictionary from the first time period to at least onevideo camera capturing video or to a back-end server communicativelycoupled to the at least one video camera based on a plurality offactors; capture updated keywords as the incident of interest unfolds ata second time period; update the incident-specific dictionary with theupdated keywords; and provide the updated incident-specific dictionaryto another video camera or to a back-end server communicatively coupledto the another video camera based on the plurality of factors.
 15. TheCAD system of claim 14, wherein the plurality of factors comprise any ofdirect responders of the incident of interest, nearby responders of theincident of interest, location based on the location-related keywords,and probable routes based on the location-related keywords.
 16. The CADsystem of claim 14, wherein the instructions that, when executed,further cause the processor to: receive an alert from one of a pluralityof video cameras or associated back-end server that a keyword from theincident-specific dictionary or the updated incident-specific dictionaryhas been detected in captured video, wherein the alert is generatedbased on video analytics using the incident-specific dictionary.
 17. TheCAD system of claim 14, wherein the instructions that, when executed,further cause the processor to: receive captured video from the at leastone video camera or associated back-end server with the captured videotagged with keywords from the incident-specific dictionary; and performvideo analytics on the captured video using the incident-specificdictionary associated therewith.